Separation science plays a key role for enhancing the performance of mass spectrometry (MS)-based analysis of complex biological samples in the field of emerging comprehensive metabolite profiling initiatives (i.e., metabolomics). A major challenge in metabolomics is the chemical diversity and wide dynamic range of the “metabolome” that remains largely uncharacterized. Consequently, high efficiency separations are used to provide greater selectivity, better quantitative reliability as well as higher quality mass spectra for qualitative identification becomes difficult to realize.
In various chromatographic techniques of separation (for example, liquid chromatography, gas chromatography etc.), sample throughput is limited since such techniques typically rely on a “single” sample injection. Consequently, the analysis time tends to be very long (for example, greater than 15 min per run). Furthermore, in such techniques, and especially when used in major metabolomics studies, major efforts are devoted to quality assurance (for example, quality control samples) and data pre-processing (for example, time alignment) to correct for long-term instrumental drift, which tend also to be time consuming, resource-intensive and subject to bias.